The main purpose in this paper is to study to what extent accessibility to R&D can explain patent production. Therefore a knowledge production function is estimated both on aggregated level and for different industrial sectors. The output of the knowledge production is the number patent applications in Swedish municipalities from 1994 to 1999. In order to account for the importance of proximity, the explanatory variables are expressed as accessibilities to university and company R&D. The total accessibility is then decomposed into local, intra-regional and inter-regional accessibility to R&D. As often is the case with R&D outputs, the regional distribution of patents is highly skewed with influential outliers. The estimations are therefore conducted with quantile regressions. The main results on aggregated level indicate that high accessibility (local) to company R&D has the greatest positive effects on patent production. The effects are statistically significant for municipalities with a patent production corresponding to the median and to quantiles above the median. Local accessibility to university R&D is only of importance for certain industrial sectors and not on aggregated level. There is also evidence that intra-regional accessibility to company R&D affects patent production positively. A conclusion is that concentrated R&D investments in companies situated in municipalities with a high patenting activity would not only gain the municipalities themselves, but also the patent production in other municipalities in the functional region.

Using the taxonomy by Anselin (2003), this paper investigates how the inclusion of spatially discounted variables on the ‘right-hand-side’ (RHS) in empirical spatial models affects the extent of spatial autocorrelation. The basic proposition is that the inclusion of inputs external to the spatial observation in question as a separate variable reveals spatial dependence via the parameter estimate. One of the advantages of this method is that it allows for a direct interpretation. The paper also tests to what extent significance of the estimated parameters of the spatially discounted explanatory variables can be interpreted as evidence of spatial dependence. Additionally, the paper advocates the use of the accessibility concept for spatial weights. Accessibility is related to spatial interaction theory and can be motivated theoretically by adhering to the preference structure in random choice theory. Monte Carlo Simulations show that the coefficient estimates of the accessibility variables are significantly different from zero in the case of modelled effects. The rejection frequency of the three typical tests (Moran’s I, LM-lag and LM-err) is significantly reduced when these additional variables are included in the model. When the coefficient estimates of the accessibility variables are statistically significant, it suggests that problems of spatial autocorrelation are significantly reduced. Significance of the accessibility variables can be interpreted as spatial dependence